Essays on the contextual determinants of demographic processes and family dynamics

This thesis consists of three self-contained articles studying whether and how different social contexts shape population and family dynamics. In the first article, I retest the Trivers–Willard hypothesis, which argues for a negative correlation between maternal stress and sex ratio at birth (SRB), with 243 years of time series data from Sweden. I find no supportive evidence for the hypothesis because the associations of SRB with most of the covariates used as proxies for maternal stress are not statistically significant, and in many cases the level of maternal stress is indeed positively correlated with SRB. In the second article, I exploit quasi-experimental variations in the duration of exposure to a school stipend project to identify the effect of maternal education on child mortality in Bangladesh. Using birth history data from the Demographic and Health Surveys, I find that an additional year of maternal schooling reduces both under-five and infant mortality by about 20%. I also document a number of mechanisms, including greater wealth and literacy, positive assortative mating, lower fertility, delayed marriage and childbearing, greater health-related knowledge, better health-seeking behaviours, and female empowerment, but not female employment. In the third article, I combine individual-level data from the Multiple Indicator Cluster Survey and province-level data on wartime bombing to assess the long-term impact of the Vietnam War on Vietnamese women's attitudes towards intimate partner violence (IPV). To establish a causal link, I use a province's distance to the arbitrarily drawn border between North Vietnam and South Vietnam as an instrument for bombing intensity in that province. I find that women living in provinces that were heavily bombed during the Vietnam War are more likely to accept IPV, reflecting the normalisation of and desensitisation to violence in the private sphere among those who were exposed to conflict violence

Inter-object Discriminative Graph Modeling for Indoor Scene Recognition

Variable scene layouts and coexisting objects across scenes make indoor scene recognition still a challenging task. Leveraging object information within scenes to enhance the distinguishability of feature representations has emerged as a key approach in this domain. Currently, most object-assisted methods use a separate branch to process object information, combining object and scene features heuristically. However, few of them pay attention to interpretably handle the hidden discriminative knowledge within object information. In this paper, we propose to leverage discriminative object knowledge to enhance scene feature representations. Initially, we capture the object-scene discriminative relationships from a probabilistic perspective, which are transformed into an Inter-Object Discriminative Prototype (IODP). Given the abundant prior knowledge from IODP, we subsequently construct a Discriminative Graph Network (DGN), in which pixel-level scene features are defined as nodes and the discriminative relationships between node features are encoded as edges. DGN aims to incorporate inter-object discriminative knowledge into the image representation through graph convolution. With the proposed IODP and DGN, we obtain state-of-the-art results on several widely used scene datasets, demonstrating the effectiveness of the proposed approach

Vision-Language Foundation Models as Effective Robot Imitators

Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on robotics data. To this end, we derive a simple and novel vision-language manipulation framework, dubbed RoboFlamingo, built upon the open-source VLMs, OpenFlamingo. Unlike prior works, RoboFlamingo utilizes pre-trained VLMs for single-step vision-language comprehension, models sequential history information with an explicit policy head, and is slightly fine-tuned by imitation learning only on language-conditioned manipulation datasets. Such a decomposition provides RoboFlamingo the flexibility for open-loop control and deployment on low-performance platforms. By exceeding the state-of-the-art performance with a large margin on the tested benchmark, we show RoboFlamingo can be an effective and competitive alternative to adapt VLMs to robot control. Our extensive experimental results also reveal several interesting conclusions regarding the behavior of different pre-trained VLMs on manipulation tasks. We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy.Comment: Fix typos. Project page: https://roboflamingo.github.i

Iron-modified biochar and water management regime-induced changes in plant growth, enzyme activities, and phytoavailability of arsenic, cadmium and lead in a paddy soil

The aim of this study was to evaluate the effect of raw (RawBC) and iron (Fe)-modified biochar (FeBC) derived from Platanus orientalis Linn branches on the plant growth, enzyme activity, and bioavailability and uptake of As, Cd, and Pb by rice in a paddy soil with continuously flooded (CF) or alternately wet and dry (AWD) irrigation in a pot experiment. Application of RawBC (3%, w/w) significantly increased soil pH, while FeBC decreased it. The FeBC was more effective in reducing As and Pb bioavailability, particularly under the AWD water regime, while RawBC was more conducive in reducing Cd bioavailability under the CF water regime. The FeBC decreased As concentration, but increased concentrations of Cd and Pb in the straw and brown rice, as compared to the untreated soil. Soil catalase and urease activities were enhanced by RawBC, but decreased by FeBC treatment. The FeBC increased the grain yield by 60 and 32% in CF and AWD treatments, respectively. The FeBC can be recommended for immobilization of As in paddy soils, but a potential human health risk from Cd and Pb in FeBC-treated soils should be considered due to increased uptake and translocation of the metals to brown rice

LRMSNet: A New Lightweight Detection Algorithm for Multi-Scale SAR Objects

In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales

A Naturalistic Study of the Effect of Acupuncture on Heart-Rate Variability

Objectives: To study the effect of acupuncture on heart rate variability (HRV) by using linear and non-linear methods of analysis. Methods: 40 patients were recruited consecutively, including patients with insomnia, stomachache, diarrhea, dizziness, cervical syndrome, lower back pain, gonarthritis, peripheral facial paralysis, post-traumatic organic brain syndrome and urinary retention. Different acupoint prescriptions were used, according to the textbook for 5-years' education on traditional Chinese medicine specialty, which is used in Chinese Universities. HRV was recorded before, during, and after acupuncture. Results: Acupuncture substantially reduced variability, causing a 41% reduction in the standard deviation. Using a Fourier analysis, the variances both in the low frequency (LF) and the high frequency (HF) ranges were markedly reduced, but the LF/HF ratio (an indication of sympatho-vagal balance) was not altered. The HR was unchanged. The sample entropy, which is a measure of the complexity of time series, was significantly increased (+35%). Conclusions: Acupuncture produced a pattern of changes different from that seen in pathological conditions, where increased variability and reduced complexity is expected